# isort: skip_file
# Copyright (c) Alibaba, Inc. and its affiliates.
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# flake8: noqa

import datasets
import os
import pandas as pd
"""The MMLU dataset on ModelScope hub. READ ONLY, DO NOT MODIFY."""

_CITATION = """\
@article{hendryckstest2021,
  title={Measuring Massive Multitask Language Understanding},
  author={Dan Hendrycks and Collin Burns and Steven Basart and Andy Zou and Mantas Mazeika and Dawn Song and Jacob Steinhardt},
  journal={Proceedings of the International Conference on Learning Representations (ICLR)},
  year={2021}
}
"""

_DESCRIPTION = """\
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas
Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
"""

_HOMEPAGE = 'https://modelscope.cn/datasets/modelscope/mmlu/summary'

_LICENSE = 'MIT'

# _URL = "https://people.eecs.berkeley.edu/~hendrycks/data.tar"
_URL = 'https://modelscope.cn/api/v1/datasets/modelscope/mmlu/repo?Revision=master&FilePath=data.tar'

task_list = [
    'high_school_european_history',
    'business_ethics',
    'clinical_knowledge',
    'medical_genetics',
    'high_school_us_history',
    'high_school_physics',
    'high_school_world_history',
    'virology',
    'high_school_microeconomics',
    'econometrics',
    'college_computer_science',
    'high_school_biology',
    'abstract_algebra',
    'professional_accounting',
    'philosophy',
    'professional_medicine',
    'nutrition',
    'global_facts',
    'machine_learning',
    'security_studies',
    'public_relations',
    'professional_psychology',
    'prehistory',
    'anatomy',
    'human_sexuality',
    'college_medicine',
    'high_school_government_and_politics',
    'college_chemistry',
    'logical_fallacies',
    'high_school_geography',
    'elementary_mathematics',
    'human_aging',
    'college_mathematics',
    'high_school_psychology',
    'formal_logic',
    'high_school_statistics',
    'international_law',
    'high_school_mathematics',
    'high_school_computer_science',
    'conceptual_physics',
    'miscellaneous',
    'high_school_chemistry',
    'marketing',
    'professional_law',
    'management',
    'college_physics',
    'jurisprudence',
    'world_religions',
    'sociology',
    'us_foreign_policy',
    'high_school_macroeconomics',
    'computer_security',
    'moral_scenarios',
    'moral_disputes',
    'electrical_engineering',
    'astronomy',
    'college_biology',
]


class MMLUConfig(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        super().__init__(version=datasets.Version('1.0.0'), **kwargs)


class MMLU(datasets.GeneratorBasedBuilder):
    BUILDER_CONFIGS = [MMLUConfig(name=task_name, ) for task_name in task_list]

    def _info(self):
        features = datasets.Features({
            'input': datasets.Value('string'),
            'A': datasets.Value('string'),
            'B': datasets.Value('string'),
            'C': datasets.Value('string'),
            'D': datasets.Value('string'),
            'target': datasets.Value('string'),
        })
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URL)
        task_name = self.config.name
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    'filepath': os.path.join(data_dir, 'data', 'test', f'{task_name}_test.csv'),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    'filepath': os.path.join(data_dir, 'data', 'val', f'{task_name}_val.csv'),
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    'filepath': os.path.join(data_dir, 'data', 'dev', f'{task_name}_dev.csv'),
                },
            ),
        ]

    def _generate_examples(self, filepath):
        df = pd.read_csv(filepath)
        df.columns = ['input', 'A', 'B', 'C', 'D', 'target']

        for i, instance in enumerate(df.to_dict(orient='records')):
            yield i, instance
